SoroushVahidi/scidocs huggingface dataset:
Rise and fall patterns of information diffusion: model and implications
(2012)

The recent explosion in the adoption of search engines and new media such as blogs and Twitter have facilitated faster propagation of news and rumors. How quickly does a piece of news spread over these media? How does its popularity diminish over time? Does the rising and falling pattern follow a simple universal law?
 In this paper, we propose SpikeM, a concise yet flexible analytical model for the rise and fall patterns of influence propagation. Our model has the following advantages: (a) unification power: it generalizes and explains earlier theoretical models and empirical observations; (b) practicality: it matches the observed behavior of diverse sets of real data; (c) parsimony: it requires only a handful of parameters; and (d) usefulness: it enables further analytics tasks such as fore- casting, spotting anomalies, and interpretation by reverse- engineering the system parameters of interest (e.g. quality of news, count of interested bloggers, etc.).
 Using SpikeM, we analyzed 7.2GB of real data, most of which were collected from the public domain. We have shown that our SpikeM model accurately and succinctly describes all the patterns of the rise-and-fall spikes in these real datasets.
Electrical stimulation of a small brain area reversibly disrupts consciousness
(2014)

The neural mechanisms that underlie consciousness are not fully understood. We describe a region in the human brain where electrical stimulation reproducibly disrupted consciousness. A 54-year-old woman with intractable epilepsy underwent depth electrode implantation and electrical stimulation mapping. The electrode whose stimulation disrupted consciousness was between the left claustrum and anterior-dorsal insula. Stimulation of electrodes within 5mm did not affect consciousness. We studied the interdependencies among depth recording signals as a function of time by nonlinear regression analysis (h(2) coefficient) during stimulations that altered consciousness and stimulations of the same electrode at lower current intensities that were asymptomatic. Stimulation of the claustral electrode reproducibly resulted in a complete arrest of volitional behavior, unresponsiveness, and amnesia without negative motor symptoms or mere aphasia. The disruption of consciousness did not outlast the stimulation and occurred without any epileptiform discharges. We found a significant increase in correlation for interactions affecting medial parietal and posterior frontal channels during stimulations that disrupted consciousness compared with those that did not. Our findings suggest that the left claustrum/anterior insula is an important part of a network that subserves consciousness and that disruption of consciousness is related to increased EEG signal synchrony within frontal-parietal networks.
A Comprehensive Framework for Image Inpainting
(2010)

Inpainting is the art of modifying an image in a form that is not detectable by an ordinary observer. There are numerous and very different approaches to tackle the inpainting problem, though as explained in this paper, the most successful algorithms are based upon one or two of the following three basic techniques: copy-and-paste texture synthesis, geometric partial differential equations (PDEs), and coherence among neighboring pixels. We combine these three building blocks in a variational model, and provide a working algorithm for image inpainting trying to approximate the minimum of the proposed energy functional. Our experiments show that the combination of all three terms of the proposed energy works better than taking each term separately, and the results obtained are within the state-of-the-art.
A Computational Model of Plan-Based Narrative Conflict at the Fabula Level
(2014)

Conflict is an essential element of interesting stories. In this paper, we operationalize a narratological definition of conflict and extend established narrative planning techniques to incorporate this definition. The conflict partial order causal link planning algorithm (CPOCL) allows narrative conflict to arise in a plan while maintaining causal soundness and character believability. We also define seven dimensions of conflict in terms of this algorithm's knowledge representation. The first three-participants, reason, and duration-are discrete values which answer the “who?” “why?” and “when?” questions, respectively. The last four-balance, directness, stakes, and resolution-are continuous values which describe important narrative properties that can be used to select conflicts based on the author's purpose. We also present the results of two empirical studies which validate our operationalizations of these narrative phenomena. Finally, we demonstrate the different kinds of stories which CPOCL can produce based on constraints on the seven dimensions.
Explanation-Based Learning: A Problem Solving Perspective
(1989)

This article outlines explanation-based learning (EBL) and its role in improving problem-solving performance through experience. Unlike inductive systems, which learn by abstracting common properties from multiple examples, EBL systems explain why a particular example is an instance of a concept. The explanations are then converted into operational recognition rules. In essence, the EBL approach is analytical and knowledge-intensive, whereas inductive methods are empirical and knowledge-poor. This article focuses on extensions of the basic EBL method and their integration with the PRODIGY problem-solving system. PRODIGY'S EBL method is specifically designed to acquire search control rules that are effective in reducing total search time for complex task domains. Domain-specific search control rules are learned from successful problem-solving decisions, costly failures, and unforeseen goal interactions. The ability to specify multiple learning strategies in a declarative manner enables EBL to serve as a general technique for performance improvement. PRODIGY'S EBL method is analyzed, illustrated with several examples and performance results, and compared with other methods for integrating EBL and problem solving. 'Present address: Artificial Intelligence Research Branch, NASA Ames Research Center, Sterling Federal Systems, Mail Stop 244-17, Moffett Field CA 94035. This research was sponsored in part by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 4976, Amendment 20, under contract number F33615-87-C-1499, monitored by the Air Force Avionics Laboratory, in part by the Office of Naval Research under contracts N00014-84-K-0345 (N91) and N00014-86-K-0678-N123, in part by NASA under contract NCC 2-463, in part by the Army Research Institute under contract MDA903-85-C-0324, under subcontract 487650-25537 through the University of California, Irvine, and in part by small contributions from private institutions. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of DARPA, ONR, NASA, ARI, or the US Government. The first and fifth authors were supported by AT&T Bell Labs Ph.D. Scholarships. Table of
Narrative Planning: Balancing Plot and Character
(2010)

Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning is narrative generation, the automated creation of meaningful event sequences. There are many factors – logical and aesthetic – that contribute to the success of a narrative artifact. Central to this success is its understandability. We argue that the following two attributes of narratives are universal: (a) the logical causal progression of plot, and (b) character believability. Character believability is the perception by the audience that the actions performed by characters do not negatively impact the audience’s suspension of disbelief. Specifically, characters must be perceived by the audience to be intentional agents. In this article, we explore the use of refinement search as a technique for solving the narrative generation problem – to find a sound and believable sequence of character actions that transforms an initial world state into a world state in which goal propositions hold. We describe a novel refinement search planning algorithm – the Intent-based Partial Order Causal Link (IPOCL) planner – that, in addition to creating causally sound plot progression, reasons about character intentionality by identifying possible character goals that explain their actions and creating plan structures that explain why those characters commit to their goals. We present the results of an empirical evaluation that demonstrates that narrative plans generated by the IPOCL algorithm support audience comprehension of character intentions better than plans generated by conventional partial-order planners.
ISO/IEC 27000, 27001 and 27002 for Information Security Management
(2013)

With the increasing significance of information technology, there is an urgent need for adequate measures of information security. Systematic information security management is one of most important initiatives for IT management. At least since reports about privacy and security breaches, fraudulent accounting practices, and attacks on IT systems appeared in public, organizations have recognized their responsibilities to safeguard physical and information assets. Security standards can be used as guideline or framework to develop and maintain an adequate information security management system (ISMS). The standards ISO/IEC 27000, 27001 and 27002 are international standards that are receiving growing recognition and adoption. They are referred to as “common language of organizations around the world” for information security [1]. With ISO/IEC 27001 companies can have their ISMS certified by a third-party organization and thus show their customers evidence of their security measures.
A 0.3 pJ/access 8T data-aware SRAM utilizing column-based data encoding for ultra-low power applications
(2016)

This paper proposes an 8T SRAMs utilizing a column-based data encoding scheme to reduce read and write power when there are similarities between consecutive data. It is useful in image processing applications where nearby pixels tend to have similar values. The proposed design has two modes of operation: normal and sequential modes. In the normal mode, it operates as a normal SRAM. In the sequential mode, bit-wise differences between consecutive data are written instead of the actual data. This leads to a much higher number of zeros in the array. Accordingly, a new data-aware bitline pre-charge scheme is proposed to minimize write power when writing a zero. A PVT-tracking reference voltage generator is also employed to compensate the read-bitline leakage for ultra-low voltage operation. On average, it offers more than 30% WBL switching power reduction. A 32Kb SRAM implemented in 65 nm CMOS process demonstrated successful operation down to 0.36 V. The total power consumption is 0.37 μW, corresponding to the maximum frequency of 0.25 MHz. Its minimum energy is 0.3 pJ/access achieved at 0.5 V.
Slotless Bearingless Disk Drive for High-Speed and High-Purity Applications
(2014)

In this paper, a bearingless drive for high-speed applications with high purity and special chemical demands is introduced. To achieve high rotational speeds with low losses, a slotless bearingless disk drive with toroidal windings is used. We present the working principle of the bearingless drive as well as a model for calculating the achievable drive torque. An advantageous winding system for independent force and torque generation is proposed, which can be realized with standard inverter technology. Additionally, the winding inductances are examined to evaluate the dynamic properties of bearing and drive. The findings are verified with simulation results and the system performance is successfully demonstrated on an experimental prototype, which runs up to 20 000 rpm and is designed for an output power of 1 kW.
Integrating low-rank and group-sparse structures for robust multi-task learning
(2011)

Multi-task learning (MTL) aims at improving the generalization performance by utilizing the intrinsic relationships among multiple related tasks. A key assumption in most MTL algorithms is that all tasks are related, which, however, may not be the case in many real-world applications. In this paper, we propose a robust multi-task learning (RMTL) algorithm which learns multiple tasks simultaneously as well as identifies the irrelevant (outlier) tasks. Specifically, the proposed RMTL algorithm captures the task relationships using a low-rank structure, and simultaneously identifies the outlier tasks using a group-sparse structure. The proposed RMTL algorithm is formulated as a non-smooth convex (unconstrained) optimization problem. We propose to adopt the accelerated proximal method (APM) for solving such an optimization problem. The key component in APM is the computation of the proximal operator, which can be shown to admit an analytic solution. We also theoretically analyze the effectiveness of the RMTL algorithm. In particular, we derive a key property of the optimal solution to RMTL; moreover, based on this key property, we establish a theoretical bound for characterizing the learning performance of RMTL. Our experimental results on benchmark data sets demonstrate the effectiveness and efficiency of the proposed algorithm.
A clinical and genetic study of the Say/Barber/Biesecker/Young-Simpson type of Ohdo syndrome.
(2008)

We report a series of eight patients with the Say/Barber/Biesecker/Young-Simpson (SBBYS) type of Ohdo syndrome, which is the largest cohort described to date. We expand on the type, frequency and severity of the clinical characteristics in this condition; comment on the natural history of Ohdo syndrome and further refine previously published diagnostic criteria. Cytogenetic investigations and microarray CGH analysis undertaken in this cohort of patients failed to identify a chromosomal aetiology. It remains possible that this rare condition is heterogeneous and therefore caution must be undertaken during counselling until the underlying genetic mechanism(s) is (are) identified.
WiFi RSS fingerprinting indoor localization for mobile devices
(2016)

While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, indoor localization with no pre-deployment effort in an indoor space, such as an office building corridor, with WiFi coverage but no a priori knowledge of the placement of the access points(APs) is implemented for mobile devices. WiFi Received Signal Strength(RSS) in the considered environment is used to build radio maps using WiFi fingerprinting approach. An offline RSS fingerprint of the environment is compared with an online RSS measurement to estimate the location of a user. Two architectures are developed based on this localization algorithm. The first one involves a client-server approach where the localization algorithm runs on the server whereas the second one is a standalone architecture and the algorithm runs on the SD card of the mobile device. Experimental results in the considered environment validate the approach for both architectures.
The Multi-User Security of Authenticated Encryption: AES-GCM in TLS 1.3
(2016)

We initiate the study of multi-user (mu) security of authenticated encryption (AE) schemes as a way to rigorously formulate, and answer, questions about the “randomized nonce” mechanism proposed for the use of the AE scheme GCM in TLS 1.3. We (1) Give definitions of mu ind (indistinguishability) and mu kr (key recovery) security for AE (2) Characterize the intent of nonce randomization as being improved mu security as a defense against mass surveillance (3) Cast the method as a (new) AE scheme RGCM (4) Analyze and compare the mu security of both GCM and RGCM in the model where the underlying block cipher is ideal, showing that the mu security of the latter is indeed superior in many practical contexts to that of the former, and (5) Propose an alternative AE scheme XGCM having the same efficiency as RGCM but better mu security and a more simple and modular design.
Vestibular papillomatosis as a normal vulvar anatomical condition.
(2013)

At the beginning of the nineteen-eighties, vulvar vestibular papillomatosis (VVP) was thought to be a human papilloma virus (HPV) disease. Based upon these findings many clinicians have been treating this condition with laser ablation or by topical application of podophyllin or trichloroacetic acid. Currently, most authors believe that VVP should be considered an anatomical variant of the vestibular mucosa and not HPV related. We present a case of VVP in which there was no histological or molecular evidence of HPV; unnecessary treatment should be avoided.
Motion-based grasp selection: Improving traditional control strategies of myoelectric hand prosthesis
(2015)

This paper introduces a novel prosthetic hand control architecture using inertial information for grasp prediction in order to reduce the cognitive burden of amputees. A pair of inertial measurement sensors (IMUs) are fitted on the wrist and bicep to record arm trajectory when reaching to grasp an object. Each object class can be associated with different methods for grasping and manipulation. An observation experiment was conducted to find the most common grasping methods for generic object classes: Very Small (VS), Small (S), and Medium (M). A Cup (CP) class was also examined to find differences in grasping habits for pick and place, and drinking applications. The resulting grasps were used to test the discriminatory ability of inertial motion features in the upper limb for VS, S and CP object classes. Subject experiments demonstrated an average classification success rate of 90.8%, 69.2% and 88.1% for VS, S and CP classes respectively when using a k-nearest neighbors algorithm with a Euclidean distance metric. The results suggest that inertial motion features have great potential to predict the grasp pattern during reach, and to the authors' knowledge, is the first IMU-based control strategy to utilize natural motion that is aimed at hand prosthesis control.
Power-constrained CMOS scaling limits
(2002)

The scaling of CMOS technology has progressed rapidly for three decades, but may soon come to an end because of powerdissipation constraints. The primary problem is static power dissipation, which is caused by leakage currents arising from quantum tunneling and thermal excitations. The details of these effects, along with other scaling issues, are discussed in the context of their dependence on application. On the basis of these considerations, the limits of CMOS scaling are estimated for various application scenarios.
Rejecting Motion Outliers for Efficient Crowd Anomaly Detection
(2019)

Crowd anomaly detection is a key research area in vision-based surveillance. Most of the crowd anomaly detection algorithms are either too slow, bulky, or power-hungry to be applicable for battery-powered surveillance cameras. In this paper, we present a new crowd anomaly detection algorithm. The proposed algorithm creates a feature for every superpixel that includes the contribution from the neighboring superpixels only if their direction of motion conforms with the dominant direction of motion in the region. We also propose using univariate Gaussian discriminant analysis with the K-means algorithm for classification. Our method provides superior accuracy over numerous deep learning-based and handcrafted feature-based approaches. We also present a low-power FPGA implementation of the proposed method. The algorithm is developed such that features are extracted over non-overlapping pixels. This allows gating inputs to numerous modules resulting in higher power efficiency. The maximum energy required per pixel is 2.43 nJ in our implementation. 126.65 Mpixels can be processed per second by the proposed implementation. The speed, power, and accuracy performance of our method make it competitive for surveillance applications, especially battery-powered surveillance cameras.
A Four-Switch Single-Stage Single-Phase Buck–Boost Inverter
(2017)

This paper proposes a single-phase, single-stage buck–boost inverter for photovoltaic (PV) systems. The presented topology has one common terminal in input and output ports which eliminates common mode leakage current problem in the grid-connected PV applications. Although it uses four switches, its operation is bimodal and only two switches receive high-frequency pulse width modulation signals in each mode. Its principle of operation is described in detail with the help of equivalent circuits. Its dynamic model is presented, based on which a bimodal controller is designed. Experimental results, in stand-alone and grid-connected mode, obtained with a 300-W laboratory prototype are presented to validate its performance.
Vehicle Number Plates Detection and Recognition using improved Algorithms : A Review with Tanzanian Case study
(2015)

— Invented in 1976, Number Plates Recognition (NPR) has since found wide commercial applications, making its research prospects challenging and scientifically interesting. A complete NPR system functions by vz steps, license plate; localization, sizing and orientation, normalization, character recognitions and geometric analysis. This paper is a review of NPR preliminary stages; it explains number plate localization, sizing and orientations as well as normalizations sections of the Number Plates Detection and Recognition-Tanzania Case study. MATLAB R2012b is employed in these processes. The input incorporated includes front and rear photographic images of vehicles, for proximity and simulation purposes the ample angle of image is 90 degree +-15. The captured image is converted to gray scale, binarized and edge detection algorithms are used to enhance edges. The output of this stage provides the input feature extraction, segmentation and
Featureless Visual Processing for SLAM in Changing Outdoor Environments
(2012)

Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features.